Channel-compensated low-level features for speaker recognition

ABSTRACT

A system for generating channel-compensated features of a speech signal includes a channel noise simulator that degrades the speech signal, a feed forward convolutional neural network (CNN) that generates channel-compensated features of the degraded speech signal, and a loss function that computes a difference between the channel-compensated features and handcrafted features for the same raw speech signal. Each loss result may be used to update connection weights of the CNN until a predetermined threshold loss is satisfied, and the CNN may be used as a front-end for a deep neural network (DNN) for speaker recognition/verification. The DNN may include convolutional layers, a bottleneck features layer, multiple fully-connected layers, and an output layer. The bottleneck features may be used to update connection weights of the convolutional layers, and dropout may be applied to the convolutional layers.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.17/107,496, filed Nov. 30, 2020, which is a continuation U.S. patentapplication Ser. No. 16/505,452, filed Jul. 8, 2019, which is acontinuation of U.S. patent application Ser. No. 15/709,024, filed Sep.9, 2017, which claims domestic benefit, under 35 U.S.C. § 119, of U.S.Provisional Application No. 62/396,617 filed Sep. 19, 2016, entitled“Improvements of GMM-Based Modeling for Speaker Recognition,” and U.S.Provisional Application No. 62/396,670, filed Sep. 19, 2016, entitled“Improvements of Speaker recognition in the Call Center,” each of whichis incorporated by reference in its entirety.

TECHNICAL FIELD

This application is related to methods and systems for audio processing,and more particularly to audio processing for speaker identification.

BACKGROUND

Current state-of-the art approaches to speaker recognition are based ona universal background model (UBM) estimated using either acousticGaussian mixture modeling (GMM) or phonetically-aware deep neuralnetwork architecture. The most successful techniques consist of adaptingthe UBM model to every speech utterance using the total variabilityparadigm. The total variability paradigm aims to extract alow-dimensional feature vector known as an “i-vector” that preserves thetotal information about the speaker and the channel. After applying achannel compensation technique, the resulting i-vector can be considereda voiceprint or voice signature of the speaker.

One drawback of such approaches is that, in programmatically determiningor verifying the identity of a speaker by way of a speech signal, aspeaker recognition system may encounter a variety of elements that cancorrupt the signal. This channel variability poses a real problem toconventional speaker recognition systems. A telephone user's environmentand equipment, for example, can vary from one call to the next.Moreover, telecommunications equipment relaying a call can vary evenduring the call.

In a conventional speaker recognition system a speech signal is receivedand evaluated against a previously enrolled model. That model, however,typically is limited to a specific noise profile including particularnoise types such as babble, ambient or HVAC (heat, ventilation and airconditioning) and/or a low signal-to-noise ratio (SNR) that can eachcontribute to deteriorating the quality of either the enrolled model orthe prediction of the recognition sample. Speech babble, in particular,has been recognized in the industry as one of the most challenging noiseinterference due to its speaker/speech like characteristics.Reverberation characteristics including high time-to-reverberation at 60dB (T60) and low direct-to-reverberation ratio (DRR) also adverselyaffect the quality of a speaker recognition system. Additionally, anacquisition device may introduce audio artifacts that are often ignoredalthough speaker enrollment may use one acquisition device while testingmay utilize a different acquisition device. Finally, the quality oftranscoding technique(s) and bit rate are important factors that mayreduce effectiveness of a voice biometric system.

Conventionally, channel compensation has been approached at differentlevels that follow spectral feature extraction, by either applyingfeature normalization, or by including it in the modeling or scoringtools such as Nuisance Attribute Projection (NAP) (see Solomonoff, etal., “Nuisance attribute projection,” Speech Communication, 2007) orProbabilistic Linear Discriminant Analysis (PLDA) (see Prince, et al.,“Probabilistic Linear Discriminant Analysis for Inferences aboutIdentity,” IEEE ICCV, 2007).

A few research attempts have looked at extracting channel-robustlow-level features for the task of speaker recognition. (See, e.g.,Richardson et al. “Channel compensation for speaker recognition usingMAP adapted PLDA and denoising DNNs,” Proc. Speaker Lang. Recognit.Workshop, 2016; and Richardson, et al. “Speaker Recognition Using Realvs Synthetic Parallel Data for DNN Channel Compensation,” INTERSPEECH,2016). These attempts employ a denoising deep neural network (DNN)system that takes as input corrupted Mel frequency cepstrum coefficients(MFCCs) and provides as output a cleaner version of these MFCCs.However, they do not fully explore the denoising DNN by applying itdirectly to the audio signal. A significant portion of relevantspeaker-specific information is already lost after MFCC extraction ofthe corrupted signal, and it is difficult to fully cover thisinformation by the DNN.

Other conventional methods explore using phonetically-aware featuresthat are originally trained for automatic speech recognition (ASR) tasksto discriminate between different senones. (See Zhang et al. “ExtractingDeep Neural Network Bottleneck Features using Low-rank MatrixFactorization,” IEEE ICASSP, 2014.) Combining those features with MFCCsmay increase performance. However, these features are computationallyexpensive to produce: they depend on a heavy DNN-based automatic speechrecognition (ASR) system trained with thousands of senones on the outputlayer. Additionally, this ASR system requires a significant amount ofmanually transcribed audio data for DNN training and time alignment.Moreover, the resulting speaker recognition will work only on thelanguage that the ASR system is trained on, and thus cannot generalizewell to other languages.

SUMMARY

The present invention is directed to a system that utilizes novellow-level acoustic features for the tasks of verifying a speaker'sidentity and/or identifying a speaker among a closed set of knownspeakers under different channel nuisance factors.

The present disclosure applies DNN directly on the raw audio signal anduses progressive neural networks instead of the simple fully-connectedneural network used conventionally. The resulting neural network isrobust to not only channel nuisance, but also to distinguish betweenspeakers. Furthermore, the disclosed augmented speech signal includestranscoding artifacts that are missing in conventional systems. Thisadditional treatment allows the disclosed speaker recognition system tocover a wide range of applications beyond the telephony channelincluding, for example, VoIP interactions and Internet of Things (IoT)voice-enabled devices such as AMAZON ECHO and GOOGLE HOME.

In an exemplary embodiment, a system for generating channel-compensatedlow level features for speaker recognition includes an acoustic channelsimulator, a first feed forward convolutional neural network (CNN), aspeech analyzer and a loss function processor. The acoustic channelsimulator receives a recognition speech signal (e.g., an utterancecaptured by a microphone), degrades the recognition speech signal toinclude characteristics of an audio channel, and outputs a degradedspeech signal. The first CNN operates in two modes. In a training modethe first CNN receives the degraded speech signal, and computes from thedegraded speech signal a plurality of channel-compensated low-levelfeatures. In a test and enrollment mode, the CNN receives therecognition speech signal and calculates from it a set ofchannel-compensated, low-level features. The speech signal analyzerextracts features of the recognition speech signal for calculation ofloss in the training mode. The loss function processor calculates theloss based on the features from the speech analyzer and thechannel-compensated low-level features from the first feed forwardconvolutional neural network, and if the calculated loss is greater thanthe threshold loss, one or more connection weights of the first CNN aremodified based on the computed loss. If, however, the calculated loss isless than or equal to the threshold loss, the training mode isterminated.

In accord with exemplary embodiments, the acoustic channel simulatorincludes one or more of an environmental noise simulator, areverberation simulator, an audio acquisition device characteristicsimulator, and a transcoding noise simulator. In accordance with someembodiments, each of these simulators may be selectably orprogrammatically configurable to perform a portion of said degradationof the recognition speech signal. In accordance with other exemplaryembodiments the acoustic channel simulator includes each of anenvironmental noise simulator, a reverberation simulator, an audioacquisition device characteristic simulator, and a transcoding noisesimulator.

In accord with exemplary embodiments, the environmental noise simulatorintroduces to the recognition speech signal at least one environmentalnoise type selected from a plurality of environmental noise types.

In accord with exemplary embodiments, the environmental noise simulatorintroduces the selected environmental noise type at a signal-to-noiseratio (SNR) selected from a plurality of signal-to-noise ratios (SNRs).

In accord with exemplary embodiments, the reverberation simulatorsimulates reverberation according to a direct-to-reverberation ratio(DRR) selected from a plurality of DRRs. Each DRR in the plurality ofDRRs may have a corresponding time-to-reverberation at 60 dB (T60).

In accord with exemplary embodiments, the audio acquisition devicecharacteristic simulator introduces audio characteristics of an audioacquisition device selectable from a plurality of stored audioacquisition device profiles each having one or more selectable audiocharacteristics.

In accord with exemplary embodiments, each audio acquisition deviceprofile of the plurality of stored audio acquisition device profiles mayinclude at least one of: a frequency/equalization characteristic, anamplitude characteristic, a filtering characteristic, an electricalnoise characteristic, and a physical noise characteristic.

In accord with exemplary embodiments, the transcoding noise simulatorselectively adds audio channel transcoding characteristics selectablefrom a plurality of stored transcoding characteristic profiles.

In accord with exemplary embodiments, each transcoding characteristicprofile may include at least one of a quantization error noisecharacteristic, a sampling rate audio artifact characteristic, and adata compression audio artifact characteristic.

In accord with exemplary embodiments, the features from the speechsignal analyzer and the channel-compensated features from the first CNNeach include a corresponding at least one of Mel-frequency cepstrumcoefficients (MFCC), low-frequency cepstrum coefficients (LFCC), andperceptual linear prediction (PLP) coefficients. That is, use by theloss function processor, the channel compensated features and thefeatures from the speech signal analyzer are of similar type (e.g., bothare MFCC).

In accord with exemplary embodiments, the system may further include asecond, speaker-aware, CNN that, in the test and enrollment modereceives the plurality of channel-compensated features from the firstCNN and extracts from the channel-compensated features a plurality ofspeaker-aware bottleneck features.

In accord with exemplary embodiments, the second CNN includes aplurality of convolutional layers and a bottleneck layer. The bottlenecklayer outputs the speaker-aware bottleneck features. The second CNN mayalso include a plurality of fully connected layers, an output layer, anda second loss function processor each used during training of the secondCNN. At least one of the fully connected layers may employ a dropouttechnique to avoid overfilling, with a dropout ratio for the dropouttechnique at about 30%. The second CNN may also include a max poolinglayer configured to pool over a time axis.

In accord with exemplary embodiments, the second CNN may take as inputat least one set of other features side by side with thechannel-compensated features, the at least one set of other featuresbeing extracted from the speech signal.

In another exemplary embodiment, a method of training a deep neuralnetwork (DNN) with channel-compensated low-level features includesreceiving a recognition speech signal; degrading the recognition speechsignal to produce a channel-compensated speech signal; extracting, usinga first feed forward convolutional neural network, a plurality oflow-level features from the channel-compensated speech signal;calculating a loss result using the channel-compensated low-levelfeatures extracted from the channel-compensated speech signal andhandcrafted features extracted from the recognition speech signal; andmodifying connection weights of the first feed forward convolutionalneural network if the computed loss is greater than a predeterminedthreshold value.

Embodiments of the present invention can be used to perform a speakerverification task in which the user inputs a self-identification, and arecognition speech signal is used to confirm that a stored identity ofthe user is the same as the self-identification. In another embodiment,the present invention can be used to perform a speaker identificationtask in which the recognition speech signal is used to identify the userfrom a plurality of potential identities stored in association withrespective speech samples. The aforementioned embodiments are notmutually exclusive, and the same low-level acoustic features may be usedto perform both tasks.

The low-level features disclosed herein are robust against various noisetypes and levels, reverberation, and acoustic artifacts resulting fromvariations in microphone acquisition and transcoding systems. Thosefeatures are extracted directly from the audio signal and preserverelevant acoustic information about the speaker. The inventivecontributions are many and include at least the following features: 1)an audio channel simulator for augmentation of speech data to include avariety of channel noise and artifacts, 2) derivation ofchannel-compensated features using a CNN, 3) an additional CNN modelemployed to generate channel-compensated features that are trained toincrease inter-speaker variance and reduce intra-speaker variance, and4) use of a multi-input DNN for increased accuracy.

While multiple embodiments are disclosed, still other embodiments willbecome apparent to those skilled in the art from the following detaileddescription, which shows and describes illustrative embodiments of theinvention. As will be realized, the invention is capable ofmodifications in various aspects, all without departing from the scopeof the present invention. Accordingly, the drawings and detaileddescription are to be regarded as illustrative in nature and notrestrictive.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram illustrating a system for performing speakerrecognition according to an exemplary embodiment of the presentdisclosure.

FIG. 2A illustrates a general structure of a deep neural network frontend in a training mode, according to exemplary embodiments of thepresent disclosure.

FIG. 2B illustrates a general structure of a deep neural network for usein testing and enrollment for a particular user, according to exemplaryembodiments of the present disclosure.

FIG. 2C illustrates a general structure of a deep neural network for usein testing and enrollment for a particular user, according to exemplaryembodiments of the present disclosure.

FIG. 3 is a block diagram illustrating elements of an acoustic channelsimulator according to exemplary embodiments of the present disclosure.

FIG. 4 is a flowchart for a method of training a channel-compensatedfeed forward convolutional neural network according to exemplaryembodiments of the present disclosure.

FIG. 5 is a flowchart adding channel noise in the method of FIG. 4 ,according to exemplary embodiments of the present disclosure.

FIG. 6 is a block diagram of an acoustic features creating systememploying a channel compensated feature generator and a second neuralnetwork for bottleneck features, according to exemplary embodiments ofthe present disclosure.

FIG. 7 is a block diagram of a speaker recognition system employing aplurality of feature generators, including a channel-compensated featuregenerator with the second neural network of FIG. 6 , according toexemplary embodiments of the present disclosure.

The above figures may depict exemplary configurations for an apparatusof the disclosure, which is done to aid in understanding the featuresand functionality that can be included in the housings described herein.The apparatus is not restricted to the illustrated architectures orconfigurations, but can be implemented using a variety of alternativearchitectures and configurations. Additionally, although the apparatusis described above in terms of various exemplary embodiments andimplementations, it should be understood that the various features andfunctionality described in one or more of the individual embodimentswith which they are described, but instead can be applied, alone or insome combination, to one or more of the other embodiments of thedisclosure, whether or not such embodiments are described and whether ornot such features are presented as being a part of a describedembodiment. Thus the breadth and scope of the present disclosure,especially in any following claims, should not be limited by any of theabove-described exemplary embodiments.

DETAILED DESCRIPTION

The detailed description set forth below in connection with the appendeddrawings is intended as a description of exemplary embodiments of thepresent disclosure and is not intended to represent the only embodimentsin which the present disclosure can be practiced. The term “exemplary”used throughout this description means “serving as an example, instance,or illustration,” and should not necessarily be construed as preferredor advantageous over other embodiments, whether labeled “exemplary” orotherwise. The detailed description includes specific details for thepurpose of providing a thorough understanding of the embodiments of thedisclosure. It will be apparent to those skilled in the art that theembodiments of the disclosure may be practiced without these specificdetails. In some instances, well-known structures and devices may beshown in block diagram form in order to avoid obscuring the novelty ofthe exemplary embodiments presented herein.

FIG. 1 is a block diagram that illustrates a system for performingspeaker recognition according to an exemplary embodiment of the presentinvention. According to FIG. 1 , a user or speaker 2 may speak anutterance into input device 10 containing an audio acquisition device,such as a microphone, for converting the uttered sound into anelectrical signal. As particularly shown in FIG. 1 , the input device 10may be a device capable of telecommunications, such as a telephone(either cellular or landline) or a computer or other processor baseddevice capable of voice over internet (VoIP) communications. In fact, itis contemplated that the present invention could be utilizedspecifically in applications to protect against, for example, telephonefraud, e.g., verifying that the caller is whom he/she claims to be, ordetecting the caller's identity as somebody on a “blacklist” or “blockedcallers list.” Although it is contemplated that the input device 10 intowhich the recognition speech signal is spoken may be a telecommunicationdevice (e.g., phone), this need not be the case. For instance, the inputdevice 10 may simply be a microphone located in close proximity to thespeaker recognition subsystem 20. In other embodiments, the input device10 may be located remotely with respect to the speaker recognitionsubsystem.

According to FIG. 1 , the user's utterance, which is used to performspeaker identification, will be referred to in this specification as the“recognition speech signal.” The recognition speech signal may beelectrically transmitted from the input device 10 to a speakerrecognition subsystem 20.

The speaker recognition subsystem 20 of FIG. 1 may include a computingsystem 22, which can be a server or a general-purpose personal computer(PC), programmed to model a deep neural network. It should be noted,however, that the computing system 22 is not strictly limited to asingle device, but instead may comprise multiple computers and/ordevices working in cooperation to perform the operations described inthis specification (e.g., training of the DNN may occur in one computingdevice, while the actual verification/identification task is performedin another). While single or multiple central processing units (CPU) maybe used as a computing device both for training and testing, graphicsprocessing units (GPU's) may also be used. For instance, the use of aGPU in the computing system 22 may help reduce the computational cost,especially during training. Furthermore, the computing system may beimplemented in a cloud computing environment using a network of remoteservers.

As shown in FIG. 1 , the speaker recognition subsystem 20 may alsoinclude a memory device 24 used for training the DNN in exemplaryembodiments. Particularly, this memory device 24 may contain a pluralityof raw and/or sampled speech signals (or “speech samples”) from multipleusers or speakers, as well as a plurality of registered voiceprints (or“speaker models”) obtained for users who have been “enrolled” into thespeaker registration subsystem 20.

In some embodiments, the memory device 24 may include two differentdatasets respectively corresponding to the respective training andtesting functions to be performed by the DNN. For example, to conducttraining the memory device 24 may contain a dataset including at leasttwo speech samples obtained as actual utterances from each of multiplespeakers. The speakers need not be enrollees or intended enrollees.Moreover, the utterances need not be limited to a particular language.For use with the system disclosed herein, these speech samples fortraining may be “clean”, i.e., including little environmental noise,device acquisition noise or other nuisance characteristics.

The memory device 24 may include another dataset to perform the“testing” function, whereby the DNN performs actual speaker recognitionby positively verifying or identifying a user. To perform this function,the dataset need only include one positive speech sample of theparticular user, which may be obtained as a result of “enrolling” theuser into the speaker recognition subsystem 22 (which will be describedin more detail below). Further, this dataset may include one or moreregistered voiceprints, corresponding to each user who can be verifiedand/or identified by the system.

Referring again to FIG. 1 , the results of the speaker recognitionanalysis can be used by an end application 30 that needs to authenticatethe caller (i.e., user), i.e., verifying that the caller is whom he/sheclaims to be by using the testing functions described herein. As analternative, the end application 30 may need to identify any caller whois on a predefined list (e.g., blacklist or blocked callers). This canhelp detect a malicious caller who spoofs a telephone number to evadedetection by calling line identification (CLID) (sometimes referred toas “Caller ID”). However, even though the present invention can be usedby applications 30 designed to filter out malicious callers, the presentinvention is not limited to those types of applications 30. Forinstance, the present invention can be advantageously used in otherapplications 30, e.g., where voice biometrics are used to unlock accessto a room, resource, etc. Furthermore, the end applications 30 may behosted on a computing system as part of computing system 20 itself orhosted on a separate computing system similar to the one described abovefor computing system 20. The end application 30 may be also implementedon a (e.g., remote) terminal with the computing system 20 acting as aserver. As another specific example, the end application 30 may behosted on a mobile device such as a smart phone that interacts withcomputing system 20 to perform authentication using the testingfunctions described herein.

It should be noted that various modifications can be made to the systemillustrated in FIG. 1 . For instance, the input device 10 may transmitthe recognition speech signal directly to the end application 30, whichin turn relays the recognition speech signal to the speaker recognitionsubsystem 20. In this case, the end application 30 may also receive someform of input from the user representing a self-identification. Forinstance, in case of performing a speaker identification task, the endapplication 30 may request the user to identify him or herself (eitheraudibly or by other forms of input), and send both the recognitionspeech signal and the user's alleged identity to the speech recognitionsubsystem 20 for authentication. In other cases, the self-identificationof the user may consist of the user's alleged telephone number, asobtained by CLID. Furthermore, there is no limitation in regard to therespective locations of the various elements illustrated in FIG. 1 . Incertain situations, the end application 30 may be remote from the user,thus requiring the use of telecommunications for the user to interactwith the end application 30. Alternatively, the user (and the inputdevice 10) may be in close proximity to the end application 30 at thetime of use, e.g., if the application 30 controls a voice-activatedsecurity gate, etc.

Channel and background noise variability poses a real problem for aspeaker recognition system, especially when there is channel mismatchbetween enrollment and testing samples. FIGS. 2A-2C illustrate a system200A for training (FIG. 2A) and using (FIGS. 2B, 2C) a CNN in order toreduce this channel mismatch due to channel nuisance factors, thusimproving the accuracy of conventional and novel speaker recognitionsystems.

The inventors have recognized that conventional speaker recognitionsystems are subject to verification/identification errors when arecognition speech signal for test significantly differs from anenrolled speech sample for the same speaker. For example, therecognition speech signal may include channel nuisance factors that werenot significantly present in the speech signal used for enrolling thatspeaker. More specifically, at enrollment the speaker's utterance may beacquired relatively free of channel nuisance factors due to use of ahigh-quality microphone in a noise-free environment, with no electricalnoise or interference in the electrical path from the microphone torecording media, and no transcoding of the signal. Conversely, at testtime the speaker could be in a noisy restaurant, speaking into alow-quality mobile phone subject to transcoding noise and electricalinterference. The added channel nuisance factors may render theresulting recognition speech signal, and any features extractedtherefrom, too different from the enrollment speech signal. Thisdifference can result in the verification/identification errors. FIGS.2A-2C illustrate a front-end system for use in the speech recognitionsubsystem 20, which is directed to immunizing the speech recognitionsubsystem against such channel nuisance factors.

The training system 200A in FIG. 2A includes an input 210, an acousticchannel simulator (also referenced as a channel-compensation device orfunction) 220, a feed forward convolutional neural network (CNN) 230, asystem analyzer 240 for extracting handcrafted features, and a lossfunction 250. A general overview of the elements of the training system200A is provided here, followed by details of each element. The input210 receives a speaker utterance, e.g., a pre-recorded audio signal oran audio signal received from a microphone. The input device 210 maysample the audio signal to produce a recognition speech signal 212. Therecognition speech signal 212 is provided to both the acoustic channelsimulator 220 and to the system analyzer 240. The acoustic channelsimulator 220 processes the recognition speech signal 212 and providesto the CNN 230 a degraded speech signal 214. The CNN 230 is configuredto provide features (coefficients) 232 corresponding to the recognitionspeech signal. In parallel, the signal analyzer 240 extracts handcraftedacoustic features 242 from the recognition speech signal 212. The lossfunction 250 utilizes both the features 232 from the CNN 230 and thehandcrafted acoustic features 242 from the signal analyzer 240 toproduce a loss result 252 and compare the loss result to a predeterminedthreshold. If the loss result is greater than the predeterminedthreshold T, the loss result is used to modify connections within theCNN 230, and another recognition speech signal or utterance is processedto further train the CNN 230. Otherwise, if the loss result is less thanor equal to the predetermined threshold T, the CNN 230 is consideredtrained, and the CNN 230 may then be used for providingchannel-compensated features to the speaker recognition subsystem 20.(See FIG. 2B, discussed in detail below.)

Turning to FIG. 3 , the acoustic channel simulator 220 includes one ormore nuisance noise simulators, including a noise simulator 310, areverberation simulator 312, an acquisition device simulator 314 and/ora transcoding noise simulator 316. Each of these simulators is discussedin turn below, and each configurably modifies the recognition speechsignal 212 to produce the degraded speech signal 214. The recognitionspeech signal 212 may be sequentially modified by each of the nuisancenoise simulators in an order typical of a real-world example such as thesequential order shown in FIG. 3 and further described below. Forexample, an utterance by a speaker in a noisy environment would becaptured with the direct environmental noises and the reflections (orreverberation) thereof. The acquisition device (e.g., microphone) wouldthen add its characteristics, followed by any transcoding noise of thechannel. It will be appreciated by those having skill in the art thatdifferent audio capturing circumstances may include a subset of nuisancefactors. Thus the acoustic channel simulator 220 may be configured touse a subset of nuisance noise simulators and/or to include affects fromeach nuisance noise simulator at variable levels.

The noise simulator 310 may add one or more kinds of environmental orbackground noise to the recognition speech signal 212. The types ofnoise may include babble, ambient, and/or HVAC noises. However,additional or alternative types of noise may be added to the signal.Each type of environmental noise may be included at a selectabledifferent level. In some embodiments the environmental noise may beadded at a level in relation to the amplitude of the recognition speechsignal 212. In a non-limiting example, any of five signal-to-noise ratio(SNR) levels may be selected: 0 dB, 5 dB, 10 dB, 20 dB and 30 dB. Inother embodiments, the selected noise type(s) may be added at aspecified amplitude regardless of the amplitude of the recognitionspeech signal. In some embodiments, noise type, level, SNR or otherenvironmental noise characteristics may be varied according to apredetermined array of values. Alternatively, each value may beconfigured across a continuous range of levels, SNRs, etc. to bestcompensate for the most typical environments encountered for aparticular application. In some exemplary embodiments, sets of noisetypes, levels, SNRs, etc., may be included in one or more environmentprofiles stored in a memory (e.g., memory 24), and the noise simulator310 may be iteratively configured according to the one or moreenvironment profiles, merged versions of two or more environmentprofiles, or individual characteristics within one or more of theenvironment profiles. In some embodiments, one or more noise types maybe added from a previously stored audio sample, while in otherembodiments, one or more noise types may be synthesized, e.g., by FMsynthesis. In experiments, the inventors mixed the recognition speechsignal 212 with real audio noise while controlling the noise level tosimulate a target SNR. Some noise types, such as fan or ambient noise,are constant (stationary) while others, such as babble, are relativelyrandom in frequency, timing, and amplitude. The different types of noisemay thus be added over an entire recognition speech signal 212, whileothers may be added randomly or periodically to selected regions of therecognition speech signal 212. After adding the one or more kinds ofenvironmental and/or background noise to the recognition speech signal212 the noise simulator 310 outputs a resulting first intermediatespeech signal 311, passed to the reverberation simulator 312.

The reverberation simulator 312 modifies the first intermediate speechsignal 311 to include a reverberation of first intermediate speechsignal, including the utterance and the environmental noise provided bythe noise simulator 310. As some environments include a different amountof reverberation for different sources of sound, in some embodiments thereverberation simulator 312 may be configured to add reverberation ofthe utterance independent from addition of reverberation ofenvironmental noise. In still other embodiments, each type of noiseadded by the noise simulator 310 may be independently processed by thereverberation simulator 312 to add a different level of reverberation.The amount and type of reverberation in real world settings is dependenton room size, microphone placement and speaker position with respect tothe room and microphone. Accordingly, the reverberation simulator may beconfigured to simulate multiple rooms and microphone setups. Forexample, the reverberation simulator may choose from (or cycle through)8 different room sizes and 3 microphone setups, for 24 differentvariations. In some embodiments, room size and microphone placement maybe configured along a continuous range of sizes and placements in orderto best compensate for the most typical settings encountered for aparticular application. The simulated reverberation may be configuredaccording to a direct-to-reverberation ratio (DRR) selected from a setof DRRs, and each DRR may have a corresponding time-to-reverberation at60 dB (T60). The reverberation simulator 312 outputs a resultant secondintermediate speech signal 313 to the acquisition device simulator 314.

The acquisition device simulator 314 may be used to simulate audioartifacts and characteristics of a variety of microphones used foracquisition of a recognition speech signal 212. As noted above speakerrecognition subsystem 20 may receive recognition speech signals 212 fromvarious telephones, computers, and microphones 10. Each acquisitiondevice 10 may affect the quality of the recognition speech signal 212 ina different way, some enhancing or decreasing amplitude of particularfrequencies, truncating the frequency range of the original utterance,some adding electrical noise, etc. The acquisition device simulator thusselectably or sequentially adds characteristics duplicating, or at leastapproximating common sets of acquisition device characteristics. Forexample, nuisance factors typical of most-popular phone types (e.g.,APPLE IPHONE® and SAMSUNG GALAXY®) may be simulated by the acquisitiondevice simulator.

The acquisition device simulator 314 may include a memory device oraccess to a shared memory device (e.g., memory 24) that stores audioacquisition device profiles. Each audio acquisition device profile mayinclude one or more audio characteristics such as those mentioned in theprevious paragraph, and which may be selectable and/or configurable. Forinstance, each audio acquisition device profile may include one or moreof a frequency/equalization characteristic, an amplitude characteristic,a filtering characteristic, an electrical noise characteristic, and aphysical noise characteristic. In some embodiments, each audioacquisition device profile may correspond to a particular audioacquisition device (e.g., a particular phone model). Alternatively, aswith the channel noise simulator 310 and the reverberation noisesimulator 312, in some embodiments each audio characteristic of anacquisition device may be selected from a predetermined set of audiocharacteristics or varied across a continuous range to provide a varietyof audio characteristics during training iterations. For example, one ormore of filter settings, amplitude level, equalization electrical noiselevel, etc. may be varied per training iteration. That is, theacquisition device simulator 314 may choose from (or cycle through) anarray of values for each acquisition device characteristic, or maychoose from (or cycle through) a set of audio acquisition deviceprofiles. In some embodiments, acquisition device characteristics may besynthesized, while in some embodiments acquisition devicecharacteristics may be stored in memory (e.g., memory 24) as an audiosample. The output of the acquisition device simulator 314 is a thirdintermediate speech signal 315 that is passed to the transcoding noisesimulator 316.

In the transcoding noise simulator 316, sets of audio encodingtechniques are applied to the third intermediate speech signal 315 tosimulate the audio effects typically added in the transcoding of anaudio signal. Transcoding varies depending on application, and mayinclude companding (dynamic range compression of the signal to permitcommunication via channel having limited dynamic range and expansion atthe receiving end) and/or speech audio coding (e.g., data compression)used in mobile or Voice over IP (VoIP) devices. In some embodiments,sixteen different audio encoding techniques may be selectivelyimplemented: four companding codecs (e.g., G.711 p-law, G.711 A-law),seven mobile codecs (e.g., AMR narrow-band, AMR wide-band (G.722.2)),and five VoIP codecs (e.g. iLBC, Speex). In some instances plural audioencoding techniques may be applied simultaneously (or serially) to thesame third intermediate speech signal 315 to simulate instances where arecognition speech signal 212 may be transcoded multiple times along itsroute. Different audio coding techniques or representative audiocharacteristics thereof may be stored in respective transcodingcharacteristic profiles. In some embodiments, the characteristicprofiles may include a quantization error noise characteristic, asampling rate audio artifact characteristic, and/or a data compressionaudio artifact characteristic. The transcoding noise simulator 316 maychoose from (or cycle through) an array of values for each audioencoding technique, or may choose from (or cycle through) thetranscoding characteristic profiles. In some embodiments, the thirdintermediate speech signal may be subjected to actual transcodingaccording to one or more of the audio transcoding techniques to generatethe degraded speech signal 214.

The acoustic channel simulator 220 may be configured to iterativelytrain the first CNN 230 multiple times for each recognition speechsignal of multiple recognition speech signals, changing noisecharacteristics for each iteration, or to successively train the firstCNN 230 using a plurality of recognition speech signals, eachrecognition speech signal being processed only once, but modifying atleast one noise characteristic for each recognition speech sample. Forexample, as described above, for each iteration one or morecharacteristics of environmental noise, reverberation, acquisitiondevice noise and/or transcoding noise may be modified in order tobroaden the intra-speaker variability.

Once the acoustic channel simulator 220 has generated the degradedspeech signal 214, there are two ways to use it: the first is during theoffline training of the speaker recognition system, while the second isduring speaker enrollment and speaker testing. The former uses thedegraded speech signal to train features or universal background modelsthat are not resilient to such channel variability, while the latteruses the degraded speech signal to enrich a speaker model or the testutterance with all possible channel conditions.

Returning to FIG. 2B, after the first CNN 230 is trained, the test andenrollment system 200B is in a test and enrollment of recognition speechsignals. The acoustic channel simulator 220, signal analyzer 240 andloss function processor 250 (each shown in dotted lines) need not befurther used. That is, the trained first CNN 230 may receive arecognition speech signal 212 from input 210 transparently passedthrough a dormant acoustic channel simulator 220, and may producechannel-compensated low-level features 232 for use by the remainder of aspeaker recognition subsystem 20 as passed transparently through adormant loss function processor 250. Alternatively, as illustrated inFIG. 2C, a trained channel-compensation CNN 230 may be used alone ininstances where further training would be unwarranted or rare.

The feed forward convolutional neural network 230 illustrated in FIGS.2A-C is trained to create a new set of features that are both robust tochannel variability and relevant to discriminate between speakers. Toachieve the first goal, the trained, channel-compensated CNN 230 takesas input the degraded speech signal described above and generates asoutput “clean” or channel-compensated features that matches handcraftedfeatures extracted by signal analyzer 240 from a non-degradedrecognition speech signal. The handcrafted features could be, forexample, MFCC (Mel frequency cepstrum coefficients), LFCC (linearfrequency cepstrum coefficients), PLP (Perceptual Linear Predictive),MFB (Mel-Filter Bank) or CQCC (constant Q cepstral coefficient).Specifically, “handcrafted features” may refer to features forparameters such as windows size, number of filters, etc. were tuned bymanual trial and error, often over a number of years. FIG. 2Aillustrates the training process.

The configuration of CNN 230 may include an input layer, a plurality ofconvolutional layers, a Log layer, and an output layer. In anon-limiting embodiment, the input layer may be configured to expect araw signal (e.g., recognition speech signal) of 110 milliseconds thatcorresponds to 880 samples (assuming that the sampling rate is 8 kHz).In some embodiments six convolutional layers may be utilized, with sixcorresponding max-pooling layers, each using rectified linear unit(ReLu) activation. For example convolutional layers may have aconfiguration as shown in Table 1 below.

TABLE 1 Max Pooling Convolutional layer Layer Layer Number of filtersFilter Size Stride 1 16 11 5 2 32 7 2 3 32 7 2 4 32 7 2 5 32 7 2 6 32 711

The Log layer may be an element-wise Log layer (log(X+0.01)), where X isgreater than zero (X>0). The inventors determined that inclusion of theLog Layer provides lower loss values, and higher speaker recognitionaccuracy. The offset (0.01) is included to avoid extreme cases (e.g.,where log(X)=−co) as X approaches zero. The output layer may includetwenty output units that correspond to the dimension of desired acousticfeatures (e.g., MFCC or CQCC). In at least one embodiment, batchnormalization is applied to each convolutional layer. It will beacknowledged by those of ordinary skill in the art that the number andconfiguration of convolutional and max pooling layers may be varied toachieve different results.

In experimental results, the acoustic features resulting from the aboveCNN configuration were applied to a Gaussian Mixture Model (GMM) speakerrecognition system and the recognition results compared with the samesystem employing baseline MFCC features. Results indicated significantimprovement, with a 52% relative drop in equal error rate (EER) over thesame system employing baseline MFCC features.

The signal analyzer 240 in FIG. 2A may be configured to perform spectralor cepstral analysis to produce handcrafted acoustic features, e.g.,coefficients for MFCC, constant Q cepstral coefficients (CQCC), LowFrequency Cepstral Coefficients (LFCC) or the like. These handcraftedfeatures are evaluated against the channel-compensated low-levelfeatures from the CNN 230 by the Loss function processor 250.

The loss function processor 250 receives the channel-compensatedlow-level features 232 and the handcrafted acoustic features 242 andcalculates a loss result 252. The loss function employed by the lossfunction processor 250 may include a mean squared error function.However, it will be acknowledged by those having skill in the art thatother loss functions could be employed. As noted above, the loss result252 may be used to update connection weights for nodes of the first CNN230 when the loss result is greater than a predetermined threshold. Ifthe loss result is less than or equal to the threshold, the training iscomplete. If all iterations of training are completed without satisfyingthe threshold, the training may be considered failed for the trainingset of recognition speech signals.

FIG. 4 is a flowchart for a training operation or method 400 fortraining a channel-compensated feed forward convolutional neural network(e.g., 230) according to exemplary embodiments of the presentdisclosure. The training operation 400 includes an operation foracquiring a recognition speech signal (S410). The recognition speechsignal (e.g., 212 in prior figures) may be obtained from a set ofrecognition speech signals previously stored (e.g., in memory 24),obtained from an audio acquisition device such as a microphone or set ofmicrophones, or from a remote source such as a repository having one ormore speaker recognition data sets. In the latter case, recognitionspeech signals may be obtained from a plurality of repositories. Therecognition speech signal may include raw audio recordings.

In operation S420, acoustic channel noise is added to the recognitionspeech signal to produce a degraded speech signal (such as degradedspeech signal 214 in previous figures). Operation S420 is described ingreater detail below with respect to FIG. 5 . In operation S430,channel-compensated features are generated from the degraded speechsignal by a first feed forward convolutional neural network (such as CNN230 in previous figures). In operation S440, handcrafted features (e.g.,coefficients of at least one of MFCC, LFCC, PLP, etc.) are derived fromthe recognition speech signal according to conventional methods. Inoperation S450, a loss result is calculated from the channel-compensatedfeatures and the handcrafted features. In some exemplary embodiments, amean squared error function may be used for satisfactory results.However, it is acknowledged that other loss functions may be employed.

In operation S460, the loss result is compared with a threshold loss. Ifthe calculated loss if less than or equal to the threshold, the method400 is complete, and the channel compensated feed forward convolutionalneural network is considered trained with respect to the speech signalsprovided. However, if the calculated loss is greater than the threshold,the calculated loss is used to modify connection weights (S470) of thefirst (i.e., channel compensating) CNN, and the method 400 is performedagain using a new recognition speech signal and/or changed parametersfor the acoustic channel noise. In some embodiments, (see solid arrow toS410 from S470) training of the CNN may include several passes using allrecognition speech signals, each pass using a different acoustic channelnoise configuration. In other embodiments (see dashed arrow to S420)each recognition speech signal may be processed iteratively until alldesired acoustic channel noise configurations are considered beforeprocessing a next recognition speech signal. In yet other embodiments,recognition speech signals may be processed serially, each recognitionspeech signal using a different acoustic channel noise configuration.

Those having skill in the art will recognize that the thresholdcomparison at operation S460 may alternatively consider trainingcomplete when the calculated loss is less than the threshold, andincomplete when the calculated loss is greater than or equal to thethreshold.

FIG. 5 is a flowchart providing additional detail to operation S420 toadd channel noise in the method 400 of FIG. 4 . In operation S422, arecognition speech signal may be modified to include environmental orbackground noise according to a configuration using one or moreselectable noise types at one or more respective signal-to-noise ratios(SNRs), (e.g., as described above with respect to noise simulator 310 inFIG. 3 ). In operation S424 a resulting modified speech signal may befurther modified to include reverberation according to a configurationusing one or more times-to-reverberation at 60 dB (T60) (e.g., asdescribed above with respect to reverberation simulator 312 in FIG. 3 ).In operation S426 the further modified speech signal may be yet furthermodified to include audio acquisition device characteristics e.g., audioartifacts, corresponding to one or more acquisition devices (e.g.,microphone, telephone, etc.) in different modes (e.g., as describedabove with respect to acquisition device simulator 314 in FIG. 3 ).Similarly, the signal resulting from adding acquisition device audiocharacteristics may be further modified at operation S428 to selectivelyinclude transcoding characteristics corresponding to one or more audiochannels. For example, an audio channel may utilize one or more audiocompression codecs that introduce loss of audio fidelity, and theeffects of one or more such codecs may be applied to the speech signal,e.g., as described above with respect to transcoding noise simulator 316in FIG. 3 .

As noted above, in some embodiments each recognition speech signal fortraining may be iteratively processed with per-iteration modification(s)to the acoustic channel noise configuration. The result of the acousticchannel noise adding operation S420 is a degraded speech signalappropriate for training a convolutional neural network to compensatefor channel and background noise.

It is desirable to generate acoustic features that are not only channelrobust, as is addressed by the systems described above, but alsoincrease the inter-speaker variability and decrease the intra-speakervariability. To do so, the inventors put in cascade the pre-trainedchannel-compensated CNN model described above (e.g., systems 200A-200C)with a second CNN that is speaker-aware. The second neural network model600 is illustrated in FIG. 6 .

The second neural network model 600 includes, in addition to the channelcompensated feature generator 610 (such as systems 200A-200C detailedabove), a convolutional neural network having an input layer 620,convolutional layers 630, and a max pooling layer 640 that outputsbottleneck features. For training, the second neural network model 600may additionally include one or more fully connected layers 650 and anoutput layer 660. An input layer may be two-dimensional, having a firstdimension corresponding to an audio sample length (e.g., 110milliseconds) and a second dimension corresponding to the number ofacoustic features (i.e. feature vectors) from the channel compensatedfeature generator 610 (e.g., CNN 230). In some embodiments, twoconvolutional layers 620 may employed, utilizing a scaled tanhactivation and respectively having number and size of filters of (32,(15, 20)) and (64, (3, 1)). (E.g., 32 filters of size 15×20.) The maxpooling layer 640 operates over the time axis and its output is denotedas bottleneck features. The fully connected layers 650 may include 256hidden units each and, like the convolutional layer may utilize scaledtanh for activation. The output layer 660 may have 3622 output units,each output unit corresponding to a single particular speaker intraining data. Naturally, the system may be scaled to accommodate adifferent number of speakers. To avoid overfitting, a dropout techniquemay be used in the fully connected layers 650 and output layer 660,instead of, e.g., batch normalization. In an exemplary embodiment adropout ratio may be about 30%.

Bottleneck features are a set of activations of nodes over time from abottleneck layer in a trained deep neural network (DNN). The bottlenecklayer is a hidden layer in the DNN of reduced dimension relative to theother layers (e.g., 3 nodes compared to 20). This DNN can be trained todiscriminate between different output classes such as senones, speakers,conditions, etc. Using a bottleneck layer in the DNN ensures that allinformation required to ultimately determine the posteriors at the DNN'soutput layer is restrained to a small number of nodes. (See Ferrer, etal., “Exploring the Role of Phonetic Bottleneck Features for Speaker andLanguage Recognition,” 2016 IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP), 5575-5579.)

When the bottleneck features are applied in classifying a particularspeech signal under test against models (e.g., Gaussian Mixture Model),the loss function to minimize for classification is categoricalCross-Entropy. While the fully-connected layers 650 and the output layer660 are used for training, they are discarded at test and enrollmenttimes as noted above, as only the trained CNN network need be used toextract bottleneck features that could be used independently of theback-end classifier (i.e., the fully connected layers 650 and outputlayer 660).

FIG. 7 is a block diagram of a speaker recognition system employing aplurality of feature generators 710 to input, in parallel to secondneural network 700, Feature sets 1 to N. Features 1 to N (710) mayinclude any of various handcrafted and learned features, such as MFCCs,LFCCs, filter-banks and glottal features, which were historicallydesigned to address speaker recognition problems, as well aschannel-compensated features discussed above. The improved results ofsuch technique compared to a classical score fusion technique may beabout 10%. Another advantage is that, compared with score fusionschemes, which requires scores from two or more systems, the disclosedmulti-DNN front end implements a single, standalone system, thus,reducing computational and development costs.

The second neural network 700 corresponds to the second neural network600 described above with respect to FIG. 6 , and is thus not describedagain. However, as input the second neural network 700 may receive aplurality of acoustic features sets in addition to channel compensatedfeatures from a channel-compensated feature generator 710 (such assystems 200A-200C discussed in detail above).

A possible architecture is thus similar to that of FIG. 6 but withthree-dimensional input instead of two-dimensional input, where thethird dimension defines the feature type.

In the preceding detailed description, various specific details are setforth in order to provide an understanding of the creation and use ofchannel compensated low-level features for speaker recognition, anddescribe the apparatuses, techniques, methods, systems, andcomputer-executable software instructions introduced here. However, thetechniques may be practiced without the specific details set forth inthese examples. Various alternatives, modifications, and/or equivalentswill be apparent to those skilled in the art without varying from thespirit of the introduced apparatuses and techniques. For example, whilethe embodiments described herein refer to particular features, the scopeof this solution also includes embodiments having different combinationsof features and embodiments that do not include all of the describedfeatures. Accordingly, the scope of the techniques and solutionsintroduced herein are intended to embrace all such alternatives,modifications, and variations as fall within the scope of the claims,together with all equivalents thereof. Therefore, the description shouldnot be taken as limiting the scope of the invention, which is defined bythe claims.

The present invention and particularly the speaker recognition subsystem20 generally relates to an apparatus for performing the operationsdescribed herein. This apparatus may be specially constructed for therequired purposes such as a graphics processing unit (GPU), digitalsignal processor (DSP), application specific integrated circuit (ASIC),field programmable gate array (FPGA) special purpose electronic circuit,or it may comprise a general-purpose computer selectively activated orreconfigured by a computer program stored in the computer. Such acomputer program may be stored in a computer readable storage medium,such as, but is not limited to, any type of disk including opticaldisks, CD-ROMs, magneto-optical disks, read-only memories (ROMs), randomaccess memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards,integrated memory, “cloud” storage, or any type of computer readablemedia suitable for storing electronic instructions.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general-purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct more specializedapparatus to perform the required method steps. The required structurefor a variety of these systems will appear from the description herein.In addition, the present invention is not described with reference toany particular programming language. It will be appreciated that avariety of programming languages may be used to implement the teachingsof the invention as described herein.

Terms and phrases used in this document, and variations thereof, unlessotherwise expressly stated, should be construed as open ended as opposedto limiting. As examples of the foregoing: the term “including” shouldbe read to mean “including, without limitation” or the like; the term“example” is used to provide exemplary instances of the item indiscussion, not an exhaustive or limiting list thereof; and adjectivessuch as “conventional,” “traditional,” “standard,” “known” and terms ofsimilar meaning should not be construed as limiting the item describedto a given time period or to an item available as of a given time, butinstead should be read to encompass conventional, traditional, normal,or standard technologies that may be available or known now or at anytime in the future. Likewise, a group of items linked with theconjunction “and” should not be read as requiring that each and everyone of those items be present in the grouping, but rather should be readas “and/or” unless expressly stated otherwise. Similarly, a group ofitems linked with the conjunction “or” should not be read as requiringmutual exclusivity among that group, but rather should also be read as“and/or” unless expressly stated otherwise. Furthermore, although item,elements or components of the disclosure may be described or claimed inthe singular, the plural is contemplated to be within the scope thereofunless limitation to the singular is explicitly stated. The presence ofbroadening words and phrases such as “one or more,” “at least,” “but notlimited to” or other like phrases in some instances shall not be read tomean that the narrower case is intended or required in instances wheresuch broadening phrases may be absent. Additionally, where a range isset forth, the upper and lower limitations of the range are inclusive ofall of the intermediary units therein.

The previous description of the disclosed exemplary embodiments isprovided to enable any person skilled in the art to make or use thepresent invention. Various modifications to these exemplary embodimentswill be readily apparent to those skilled in the art, and the genericprinciples defined herein may be applied to other embodiments withoutdeparting from the spirit or scope of the invention. Thus, the presentinvention is not intended to be limited to the embodiments shown hereinbut is to be accorded the widest scope consistent with the principlesand novel features disclosed herein.

What is claimed is:
 1. A computer-implemented method comprising:obtaining, by a computer, one or more enrolling speech signals for aregistered speaker during speaker registration; generating, by thecomputer, one or more degraded enrolling signals by applying an acousticchannel simulator on the one or more enrolling speech signals;extracting, by the computer, an enrolled voiceprint for the registeredspeaker by applying a trained neural network on the one or more degradedenrolling signals and the one or more enrolling speech signals;obtaining, by the computer, a test speech signal for a test speakerduring speaker recognition; extracting, by the computer,channel-compensated features from the test speech signal for the testspeaker by applying the trained neural network on the test speechsignal; and identifying, by the computer, the test speaker as theregistered speaker in response to determining that a loss between thetest voiceprint and the enrolled voiceprint satisfies a distancethreshold.
 2. The method according to claim 1, wherein the acousticchannel simulator generates a degraded enrolling speech signal having atype of degradation added to an enrolling speech signal from theregistered speaker, and wherein the type of degradation includes one ofenvironmental noise, reverberation, an audio acquisition devicecharacteristic, or an audio channel transcoding artifact.
 3. The methodaccording to claim 1, wherein generating a degraded enrolling speechsignal includes: selecting, by the computer, an environmental noise typeas a type of degradation from a plurality of environmental noise typesstored in a noise profile database; and simulating, by the computer, theenvironmental noise type in the enrolling speech signal applied by theacoustic channel simulator on an enrolling speech signal.
 4. The methodaccording to claim 1, wherein generating the degraded enrolling signalincludes simulating, by the computer, a set of one or more audioacquisition device characteristics according to an audio acquisitiondevice profile applied by the acoustic channel simulator to an enrollingspeech signal.
 5. The method according to claim 4, wherein each audioacquisition device profile includes at least one of a frequencycharacteristic, an amplitude characteristic, a filtering characteristic,an electrical noise characteristic, or a physical noise characteristic.6. The method according to claim 1, wherein generating the degradedenrolling speech signal includes: selecting, by the computer, atranscoding profile from a plurality of transcoding profiles stored in atranscoding profile database; and simulating, by the computer, a set ofaudio channel transcoding characteristics according to a transcodingprofile applied by the acoustic channel simulator to an enrolling speechsignal.
 7. The method according to claim 1, wherein generating thedegraded enrolling speech signal includes simulating, by the computer, areverberation according to a direct-to-reverberation ratio (DRR) appliedby the acoustic channel simulator to the enrolling speech signal.
 8. Themethod according to claim 1, further comprising training, by thecomputer, a neural network to minimize a loss between a set ofchannel-compensated training features extracted from a training signalby applying the neural network and a set of handcrafted trainingfeatures extracted from corresponding clean training speech signals,thereby training the trained neural network.
 9. The method according toclaim 8, further comprising applying, by the computer, the acousticchannel simulator on a clean training signal to generate the trainingsignal having a type of degradation corresponding to achannel-compensated training feature for training the trained neuralnetwork.
 10. The method according to claim 8, wherein a handcraftedtraining features includes at least one of: Mel-frequency cepstrumcoefficients (MFCCs), low-frequency cepstrum coefficients (LFCCs),perceptual linear prediction (PLP) coefficients, linear or Mel filterbanks, or glottal features.
 11. A computer-implemented methodcomprising: obtaining, by a computer, one or more training speechsignals containing corresponding training utterances; for a trainingspeech signal of the one or more training speech signals, generating, bythe computer, a degraded training speech signal having a type ofdegradation by applying a noise simulator on the training speech signal;extracting, by the computer, a set of training low-level features forthe degraded speech signal by applying a neural network on the degradedspeech signal; extracting, by the computer, a set of traininghandcrafted features for the speech signal by applying a signal analyzeron the training speech signal; and updating, by the computer, one ormore connection values of the neural network in response to determiningthat a loss result fails to satisfy a training loss threshold, the lossresult based upon the set of training low-level features and the set oftraining handcrafted features.
 12. The method according to claim 11,further comprising generating, by the computer, the loss resultaccording to a loss function using the set of training low-levelfeatures and the set of training handcrafted features.
 13. The methodaccording to claim 11, wherein, for each type of degradation of aplurality of types of degradation, the computer updates the one or moreconnection values of the neural network according to the loss functionto minimize the loss result between the set of training low-levelfeatures and the set of training handcrafted features.
 14. The methodaccording to claim 11, wherein, for each training speech of the one ormore training speech signals, the computer updates the one or moreconnection values of the neural network according to the loss functionto minimize the loss result between the set of training low-levelfeatures and the set of training handcrafted features.
 15. The methodaccording to claim 11, further comprising determining, by the computer,that a next loss result for a next training signal satisfies thetraining loss threshold; and storing, by the computer, a trained neuralnetwork from the neural network in response to determining that the nextloss result satisfies the training loss threshold.
 16. The methodaccording to claim 11, wherein generating the degraded training speechsignal having the type of degradation includes simulating, by thecomputer, an environmental noise being applied to the training speechsignal according to a type of environmental noise.
 17. The methodaccording to claim 11, wherein generating the degraded training speechsignal having the type of degradation includes simulating, by thecomputer, a reverberation being applied to the training speech signalaccording to a direct-to-reverberation ratio (DRR).
 18. The methodaccording to claim 11, wherein generating the degraded training speechsignal having the type of degradation includes simulating, by thecomputer, one or more audio device characteristics being applied to thetraining speech signal according to an audio acquisition device profile.19. The method according to claim 11, wherein generating the degradedtraining speech signal having the type of degradation includessimulating, by the computer, a set of audio channel transcodingcharacteristics being applied to the training speech signal according toa transcoding profile.
 20. The method according to claim 11, wherein thelow-level features include at least one of: Mel-frequency cepstrumcoefficients (MFCCs), low-frequency cepstrum coefficients (LFCCs),perceptual linear prediction (PLP) coefficients, linear or Mel filterbanks, or glottal features.